Source code analysis dataset

Source code analysis dataset

The data in this article pair source code with three artifacts from 108,568 projects downloaded from Github that have a redistributable license and at least 10 stars. The first set of pairs connects snippets of source code in C, C++, Java, and Python with their corresponding comments, which are extr...

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Bibliographic Details
Main Authors: Ben Gelman, Banjo Obayomi, Jessica Moore, David Slater
Format: Article
Language:English
Published: Elsevier 2019-12-01
Series:Data in Brief
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340919310674
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spelling doaj-44847eea75f948ffa9b130c94ee0490e2020-11-24T21:56:57ZengElsevierData in Brief2352-34092019-12-0127Source code analysis datasetBen Gelman0Banjo Obayomi1Jessica Moore2David Slater3Machine Learning Group, Two Six Labs, 901 N. Stuart St, Suite 1000, Arlington, VA, 22203, USAMachine Learning Group, Two Six Labs, 901 N. Stuart St, Suite 1000, Arlington, VA, 22203, USAMachine Learning Group, Two Six Labs, 901 N. Stuart St, Suite 1000, Arlington, VA, 22203, USACorresponding author.; Machine Learning Group, Two Six Labs, 901 N. Stuart St, Suite 1000, Arlington, VA, 22203, USAThe data in this article pair source code with three artifacts from 108,568 projects downloaded from Github that have a redistributable license and at least 10 stars. The first set of pairs connects snippets of source code in C, C++, Java, and Python with their corresponding comments, which are extracted using Doxygen. The second set of pairs connects raw C and C++ source code repositories with the build artifacts of that code, which are obtained by running the make command. The last set of pairs connects raw C and C++ source code repositories with potential code vulnerabilities, which are determined by running the Infer static analyzer. The code and comment pairs can be used for tasks such as predicting comments or creating natural language descriptions of code. The code and build artifact pairs can be used for tasks such as reverse engineering or improving intermediate representations of code from decompiled binaries. The code and static analyzer pairs can be used for tasks such as machine learning approaches to vulnerability discovery. Keywords: Source code, Code comments, Bug detection, Static analysishttp://www.sciencedirect.com/science/article/pii/S2352340919310674
collection DOAJ
language English
format Article
sources DOAJ
author Ben Gelman
Banjo Obayomi
Jessica Moore
David Slater
spellingShingle Ben Gelman
Banjo Obayomi
Jessica Moore
David Slater
Source code analysis dataset
Data in Brief
author_facet Ben Gelman
Banjo Obayomi
Jessica Moore
David Slater
author_sort Ben Gelman
title Source code analysis dataset
title_short Source code analysis dataset
title_full Source code analysis dataset
title_fullStr Source code analysis dataset
title_full_unstemmed Source code analysis dataset
title_sort source code analysis dataset
publisher Elsevier
series Data in Brief
issn 2352-3409
publishDate 2019-12-01
description The data in this article pair source code with three artifacts from 108,568 projects downloaded from Github that have a redistributable license and at least 10 stars. The first set of pairs connects snippets of source code in C, C++, Java, and Python with their corresponding comments, which are extracted using Doxygen. The second set of pairs connects raw C and C++ source code repositories with the build artifacts of that code, which are obtained by running the make command. The last set of pairs connects raw C and C++ source code repositories with potential code vulnerabilities, which are determined by running the Infer static analyzer. The code and comment pairs can be used for tasks such as predicting comments or creating natural language descriptions of code. The code and build artifact pairs can be used for tasks such as reverse engineering or improving intermediate representations of code from decompiled binaries. The code and static analyzer pairs can be used for tasks such as machine learning approaches to vulnerability discovery. Keywords: Source code, Code comments, Bug detection, Static analysis
url http://www.sciencedirect.com/science/article/pii/S2352340919310674
work_keys_str_mv AT bengelman sourcecodeanalysisdataset
AT banjoobayomi sourcecodeanalysisdataset
AT jessicamoore sourcecodeanalysisdataset
AT davidslater sourcecodeanalysisdataset
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